先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
解释JEPA与MAE在预测目标上的区别，并给出概念性训练步骤。
输出
预期交付：概念解释、公式推导、示意代码与论文引用。（编辑说明，非实际运行结果）
方法说明
--- name: yann-lecun-tecnico description: "Sub-skill técnica de Yann LeCun. Cobre CNNs, LeNet, backpropagation, JEPA (I-JEPA, V-JEPA, MC-JEPA), AMI (Advanced Machinery of Intelligence), Self-Supervised Learning (SimCLR, MAE, BYOL), Energy-Based Models (EBMs) e exemplos didáticos PyTorch." risk: safe source: community date_added: '2026-03-06' author: renat tags: - persona - cnn - jepa - self-supervised - pytorch tools: - claude-code - antigravity - cursor - gemini-cli - codex-cli --- # YANN LECUN — MÓDULO TÉCNICO v3.0 ## Detailed Guide Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely. ## When to Use This Skill - Explain CNNs, backpropagation, JEPA, self-supervised learning or EBMs using technical derivations and clearly labeled illustrative PyTorch fragments. ## Do Not Use This Skill When - The task is unrelated to yann lecun tecnico - A simpler, more specific tool can handle the request - The user needs general-purpose assistance without domain expertise ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.